Optimizing glass manufacturing yield with AI-based operator assistant

How to optimize manufacturing quality and yield in real-time? We delivered an AI-based Operator Assistant solution for a glass product manufacturer to increase manufacturing yield and consistency.
Glass product manufacturer

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A glass product manufacturer had been looking for ways to increase manufacturing quality and the amount of non-defective products throughout the manufacturing process. In order to achieve this goal, the quality level should be visible to all machine operators and other stakeholders.

Unikie understood the quality level of a manufactured glass product varies depending on the human operator in charge of operating the production line, as well as on the glass batch.


Managing manufacturing yield and consistency

At the start of the project, Unikie and the client made an assessment of the current situation containing aspects such as:

  • Existing process
  • Data connectivity to equipment in use
  • Available manufacturing data sets
  • Sensors in use
  • Product and batch information


Based on the information from the assessment and understanding of the key quality parameters, Unikie proposed how to minimize the impact of human operators and differences between glass batches.

No installation of new sensors, nor changes to the manufacturing process were needed during the process. Existing equipment parameters and product attributes (e.g. glass type, thickness, quality measurement) were processed with Unikie AI Vision, a framework for real-time sensor fusion powered by AI. Machine learning was also used to understand existing parameter correlation to output quality.


Optimized process control, fewer non-defective products

The solution is based on a data and complex ML-based digital twin of the production line that is used for predicting quality and generating recommendations on optimal machine settings. The project had two major outcomes for the client:

Realtime performance dashboard

Quality level monitoring for the operators and other stakeholders via a holistic realtime quality dashboard.

AI-based operator assistant

Operator assistant provides real-time tips for the operator on how to tune the process parameters to increase quality and manufacturing yield. It also enables consistent quality regardless of the operator, product or batch.

While the realtime performance dashboard provides the machine operators an overall view of the process quality and yield, the AI-based Operator assistant provides them real-time suggestions of possible process parameters before a manufacturing of a new batch is started. Operators can then adjust the process parameters accordingly to produce optimal quality and yield regardless of their experience level.

The solution was taken into use in a single factory where the amount of defective and failing glasses decreased even over 20% compared to the level before implementing the solution, increasing the overall yield in the same proportion. Based on the results, the solution is being rolled out to several other manufacturing sites.

Key results of
the project